COMPARISON STUDY OF SUBSPACE IDENTIFICATION METHODS APPLIED TO FLEXIBLE STRUCTURES

In the past few years, various time domain methods for identifying dyn amic models of mechanical structures from modal experimental data have appeared. Much attention has been given recently to so-called subspac e methods for identifying state space models. This paper presents a de tailed comparison study of these subspace identification methods: the eigensystem realisation algorithm with observer/Kalman filter Markov p arameters computed from input/output data (ERA/OM), the robust version of the numerical algorithm for subspace state space system identifica tion (N4SID), and a refined version of the past outputs scheme of the multiple-output error state space (MOESP) family of algorithms. The co mparison is performed by simulating experimental data using the five m ode reduced model of the NASA Mini-Mast structure. The general conclus ion is that for the case of white noise excitations as well as coloure d noise excitations, the N4SID/MOESP algorithms perform equally well b ut give better results (improved transfer function estimates, improved estimates of the output) compared to the ERA/OM algorithm. The key co mputational step in the three algorithms is the approximation of the e xtended observability matrix of the system to be identified, for N4SID /MOESP, or of the observer for the system to be identified, for the ER A/OM. Furthermore, the three algorithms only require the specification of one dimensioning parameter. (C) 1998 Academic Press.